{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...         V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"creditcard.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x216366d8860>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjQAAAGCCAYAAAD38Fn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3XucXWV97/HPFxCoaIKIBqzgjRbQqiXhWgtK4wEVtFZa\nJWgFvFVF5ESlthwtEXqxtBqOXCzlIioyRwv1UkCioBUFBCVoQQIqIvFCIlEIiNzzO3+sNbDZzkxm\ndiaZWeTzfr32a7LX+u21n7UnyXzneZ71rFQVkiRJXbbBVDdAkiRpTRloJElS5xloJElS5xloJElS\n5xloJElS5xloJElS5xloJElS5xloJElS5xloJElS5xloJK2xJH+f5P7pdqzJkuRZSVYleec4aqdd\n+6X1gYFGmqaSHNz+EB3p8Y9T3b4+1T6m27GmQgGrJvKCJL+b5Ogkf7CW2iQ96m001Q2QNKYC3g/8\nuG/7teu+KRqno4FjJviap7av+wF+b6WBGGik6e/Cqlo83uIkATauqnvXYps0iqpaxQR7aICsjbaM\n+82TjQCq6oGpbIe0JhxykjosyYbtENSHk/xlku8B9wBz2/3vTXJpkl8m+U2SbyV5Zd8xhueHHDTK\nsY/q2/7CJN9OcneS7yd54wTbvEeSLya5Lcmvk3wnyWGrec0bk1ycZHn7vtcmefMIdbsm+XKSFe35\n/ijJv/fVvDbJVUnuTLIyyXdX9/59r/+rJDe27fhmkp369v/WHJokL0nyjfac70xyfZJj2n1zgcto\neuPOaj/zB3u/H0kOTLK4fc9fJPl4kq1GaNuBSa5r676b5OVJzkryg56a4e/3EUneleRG4G7g95Ns\nkuTY9vO5vf3+/HeSPfve56E5RUne0X7Ov05yYZKt0zg6yU/a78O5SWaM9zOWBmEPjTT9zUzyxN4N\nVfXLvpp9gQOBk4BfAUvb7e8EzgXOAjYGDgLOTfLSqvrSRBuS5PnAF4FbaIbCNgb+Hlg+zte/BPg8\n8FPgQ8AvgGcD+7VtH83bgKvb1z4A/ClwShKq6tT22LOARcDPgX8A7gCeDryi5/1fCnyyrft3mp6R\nZwN/tJr3H3Yw8Fjg5Pa176X5PLdre2agbw5Qkue27b6K5jO7F/i99j2hGWJa0D5Opgk3AJe2r39T\n29ZvAn8NbA38b+CPkuxUVb9u614BfKr9nN4LPBE4s/08RpqT9GbgMcC/AfcBtwObA4cAQ8ApwAzg\nTcCXkuxcVd/rO8YhwIbA/wW2BI4EPt2ewx8B/wRsD7wDuBV46wjtkCZHVfnw4WMaPmh+eK4a4fFg\nT82G7bb7gO1GOMYmfc83Ar4HfLFn27PaYxzUVzt87KN6tv0XcCewVc+2Z9OEjPtWcz4bAjcD3wce\nN0bdsf3H6j+PdtuXgSU9zw8AHgSeO8axTwBuHeB7MfwZLettO/Bn7XvuM1r7gXe3NY8f4/i7jfI9\n2JgmCFwFPKZn+yva+v/Ts+064EfApj3b9m7rvj/CufwS2Lzv/TYANurbNpMmeH50hGP8DHhsz/Z/\nbrd/C9igZ/ungbt6t/nwMdkPh5yk6a1oeide3PP4XyPUXVxVP/ytF/fMo0myOc1v4N8AZk+0Ie08\nixcD51bVsp73uA64aByH2BnYBlhYba/CePWdx4y2x+prNMMkv9Puup2m1+QVSTYc5VC3AzOSjPQZ\njsfZfW3/evuezxzjNbe3X/9sgPfblaan5aSqemgYq6q+APyQpmeLJNsAOwBnVtU9PXVfBZaMcuzP\nVNXtvRuqalW182jaYaMn0PTifJuR/858uqp+0/P8ivbrJ+rhHqvh7ZsCT1nN+UoDM9BI09+3quor\nvY8Ran480guTvKKd53E3zVDUL2iGGmYO0I5ZwCY0P0j73TCO1z+LJqD1D1usVpI9k3wlya9pAsKt\nPHwl0fC5fAX4bLt9RZLPprn0feOeQ50E3AhcmGRpktOS7DOBpvyk7/lt7dcnjPGas2mGiz7WzgH6\nVJIDkoxnIvDTaD6z74+w7/p2Pz1fbxyhbqTvF4z+d+bQJNfQDI39kubvzEsY+e9M/+exsv3601G2\nj/U5SWvEQCM9OtzdvyHJ3jQ/4O+kmbvwUpoelk/zyH/7o635MlovxzqV5PdohpdmAPOBl9Gcx0fa\nkg0AqnEA8AKa4PJU4GPAFcO9OG3P0vNp5uD8F/AnNOHm1HE258HRmjnaC6rqbuCPaXrWzgL+EPgP\nmrlIU2mkvzOHAKfT9OocQjM368U0vWEj/bwY7fOY8OckrSkDjfTo9SqaeQsvqaqPV9Witnen/9/9\ncC/D5n3bn9b3fDkPT2jtt8M42nMjzQ+0iS4e9wqaYY/9qurUqrqwPY8RL0uvqm9W1fuqaheaeUjP\nB/6iZ//9VXVeVR1WVc+k+QH+hiTbTrBd49aGra9U1bur6jk0a878ryR7DZeM8tKbaT6z7UfYt327\nn56v241QN9K20RwA3FBVr66qs6vqy+1n/Ture6E01Qw00qPXgzQTNB/qaUnyTODlvUVVdRvNMM5e\nPNJh9PygbedWfBl4VZKte475B7SXia/Gt2iuvpo/wUt4h3/bf+j/q3Zux+t7i9o5Qv2+237dpK3Z\nYoSaa3prJtso7/mIdtEET/jtUHklzbDP29o5TMPHfDlNsDwPoKp+QjMEdXDPnKLhS8J3nEBzf6tn\nJckLgF0mcIwur/KsDvOybWl6W5Mu+vNpLttelGSI5nLft9PMd3lOX+1pwHuSrAQWAy+imfPS//5/\nB1wOXJrkozQ/kN9BEwr6j/kIVbUqydtphsG+k+RjNFcN7QBsX1X7j/LSRTRXz1zQDg3NoJkHdAvw\n5J66N7aXOH+O5mqf4brbgAvbmjOTPA74Ks0VOs+kCW5XVdUPWDs+kGR3miGmm4GtaL4PN/PwJdo/\noBkafHuSe2gCzuVVtTTJ39Bctn1J+318Cs339Yc8POwGcBTNJfqXJvk4zWXUb6e5LHy8Ye08mknV\n/9m291nAX9FcQTXeY4z2d9bhJq1V9tBI09t4ftsd8d5HVfVlmh/oTwGOpxl2eTftb/V9jgbOAF5N\nEx4eAPbvP3ZVfYdmgugvgQ/Q9JIcNcoxf7uhVRfQzFv5IfAemrVo9ga+MMI5Db9mCfDnNP9f/SvN\nuign0KzZ0uurNGFsHs26KO+m+UH8J20PBsAnaC5xfzvNPJuDaNZu2W88zWfk78dI23uff5Zmkuyh\nwIk0AeFiYG5V3dWe433AX7b1H6WZSPzH7b7T23ZuQvO9eSPNHJy9eq+4qqrPAa+lGR76IM1Q3cE0\nQ30PXfk01rlU1WnA+4CdaP7OzKVZ3+g7o5zjaJ/HSOy50VqVKv+OSdKjVXvF0tKqGk9okzprynto\nkry1XaJ7Zfu4rF1NtLfmmCQ/b5fQ/nKS7fr2b5LkpDTLnd+Z5JwkT+6reUJ7ueTKNMuPn5Zks76a\nbZKcn+SuJMuSHJdkg76a5yW5JM3S4jcnOXKyPxNJmqgkG43w/9WLaYYCvzo1rZLWnSkPNDTrGLyX\nZtGmOTRrSXw+yY7Q3IuGZoz+LTSLTN1FMyegd22J42m6jA+gmdj4FJqx5F5n00yOm9vW7kWztDft\n+2wAXEAzr2h3mq7aQ+i5a26Sx9OM59/UtvdIYEE7bi9JU+lpwJIkf5fkzUkW0gzl/QQY72XpUmdN\nyyGnJL8E3lNVH0vyc+Bfqmphu28GzeWjB1fVZ9rntwIHVtVn25rtadZR2L2qrmzD0feAOVV1dVuz\nL82kyadW1bI093j5ArB1Va1oa/6KZiz6SVX1QJK30SxrvlXPapr/BPxpVT17nXw4kjSC9iqvf6NZ\nh+dJNJOMLwL+tqp+PIVNk9aJ6dBD85AkGyQ5kObmb5cleQbNFQEXD9dU1R00y2jv0W7amaZXpbfm\nBprLQ4drdgduGw4zrYtoJqnt1lNzzXCYaS2iWR3zOT01lwyHmZ6a7ZMMsvKqJE2Kqrq9qg6sqm2q\natOqelJVzTPMaH0xLQJNkj9IcifNQlknA3/WhpKtaEJH/518l7f7oFmO/b426IxWsxXN8t0PqaoH\naZaC760Z6X2YYI0kSVrHpss6NNfTrOY5k+byzE/0rKDZeWlupLcvzb1T+i+flCRJo9sUeDqwqKp+\nOVrRtAg07RDOj9qnVyfZFTgCOI5mMaZZPLJnZBYwPHy0DNg4yYy+XppZ7b7hmv6rnjYEtuir6V8N\nc1bPvuGvs1ZTM5J9ada6kCRJg3ktzQU+I5oWgWYEGwCbVNVNSZbRXJn0P/DQpODdaBbFAriKZhGw\nuTQLWA1PCt6WZkVT2q+bJ9mpZx7NXJqwdEVPzVFJtuyZR7MPzV1ir+up+fskG7ZDVsM1N1TV8N1k\nR/JjgLPOOosdd5zIKuSarubPn8/ChQunuhmSRuG/0UePJUuW8LrXvQ5GuUP8sCkPNEn+kWaJ7aXA\n42kS2AtpggI0l2S/L8kPaU7mWJpVNz8PzSThJKcDH05yG83M/o8Al1bVlW3N9UkWAae2VyptTLPS\n6FB7912AL9EEl0+2l4pv3b7XiVV1f1tzNs3S72ck+WfguTRLkB+xmtO8B2DHHXdk9uzZE/+QNO3M\nnDnT76U0jflv9FFpzCkbUx5oaIaCPk4TIFbS9MTs097hlao6LsljadaM2Rz4OvDSdqnwYfNpbqp2\nDs3y4BfS3J+l10E0y45fRHPDvnPoCSLtfWb2p1l2/DKa9W7OpFkSfrjmjiT70PQOfRtYASxolyaX\nJElTZMoDTVWtdlG6qloALBhj/73A4e1jtJrbgdet5n1+QnP/mrFqrqXpQZIkSdPEtLhsW5IkaU0Y\naKQBzJs3b6qbIGkM/htd/xhopAH4n6U0vflvdP1joJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEk\nSZ1noJEkSZ1noJEkSZ035bc+0KPL0qVLWbFixeoL1Qlbbrkl22677VQ3Q5JWy0CjSbN06VK2335H\n7rnnN1PdFE2STTd9LDfcsMRQI2naM9Bo0qxYsaINM2cBO051c7TGlnDPPa9jxYoVBhpJ056BRmvB\njsDsqW6EJGk94qRgSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLU\neQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYa\nSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYaSZLU\neVMeaJL8bZIrk9yRZHmSzyb5/b6ajyVZ1fe4oK9mkyQnJVmR5M4k5yR5cl/NE5J8KsnKJLclOS3J\nZn012yQ5P8ldSZYlOS7JBn01z0tySZK7k9yc5MjJ/lwkSdL4TXmgAfYETgB2A14MPAb4UpLf6av7\nIjAL2Kp9zOvbfzywH3AAsBfwFODcvpqzgR2BuW3tXsApwzvb4HIBsBGwO3AwcAhwTE/N44FFwE3A\nbOBIYEGSN030xCVJ0uTYaKobUFUv632e5BDgF8Ac4Bs9u+6tqltHOkaSGcAbgAOr6mvttkOBJUl2\nraork+wI7AvMqaqr25rDgfOTvKeqlrX7dwD2rqoVwDVJ3g98MMmCqnoAeB1N6Hpj+3xJkp2AdwGn\nTcZnIkmSJmY69ND02xwo4Fd921/UDkldn+TkJFv07JtDE84uHt5QVTcAS4E92k27A7cNh5nWRe17\n7dZTc00bZoYtAmYCz+mpuaQNM7012yeZObFTlSRJk2FaBZokoRk6+kZVXdez64vA64E/Af4aeCFw\nQVsPzRDUfVV1R98hl7f7hmt+0buzqh6kCU69NctHOAYTrJEkSevQlA859TkZeDbwgt6NVfWZnqff\nS3INcCPwIuCr66x1a2j+/PnMnPnITpx58+Yxb17/dCBJktY/Q0NDDA0NPWLbypUrx/XaaRNokpwI\nvAzYs6puGau2qm5KsgLYjibQLAM2TjKjr5dmVruP9mv/VU8bAlv01ezS93azevYNf521mpoRLVy4\nkNmzZ49VIknSemukX/IXL17MnDlzVvvaaTHk1IaZP6WZjLt0HPVPBZ4IDAefq4AHaK5eGq7ZHtgW\nuLzddDmweTuBd9hcIMAVPTXPTbJlT80+wErgup6avdow1FtzQ1WNL0ZKkqRJNeWBJsnJwGuBg4C7\nksxqH5u2+zdr14LZLcnTkswFPgd8n2YyLm2vzOnAh5O8KMkc4Azg0qq6sq25vq0/NckuSV5Ac7n4\nUHuFE8CXaILLJ9u1ZvYFjgVOrKr725qzgfuAM5I8O8lrgHcCH1qbn5MkSRrddBhyeivNlUb/3bf9\nUOATwIPA82gmBW8O/JwmmPxdT8gAmN/WngNsAlwIHNZ3zIOAE2mublrV1h4xvLOqViXZH/gocBlw\nF3AmcHRPzR1J9gFOAr4NrAAWVNXpg5y8JElac1MeaKpqzF6iqroHeMk4jnMvcHj7GK3mdpp1ZMY6\nzk+A/VdTcy3NlVaSJGkamPIhJ0mSpDVloJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEk\nSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1n\noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEk\nSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1n\noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ1noJEkSZ035YEmyd8muTLJHUmWJ/lskt8foe6YJD9P8psk\nX06yXd/+TZKclGRFkjuTnJPkyX01T0jyqSQrk9yW5LQkm/XVbJPk/CR3JVmW5LgkG/TVPC/JJUnu\nTnJzkiMn8zORJEkTM+WBBtgTOAHYDXgx8BjgS0l+Z7ggyXuBdwBvAXYF7gIWJdm45zjHA/sBBwB7\nAU8Bzu17r7OBHYG5be1ewCk977MBcAGwEbA7cDBwCHBMT83jgUXATcBs4EhgQZI3Df4RSJKkNbHR\nVDegql7W+zzJIcAvgDnAN9rNRwDHVtV5bc3rgeXAK4HPJJkBvAE4sKq+1tYcCixJsmtVXZlkR2Bf\nYE5VXd3WHA6cn+Q9VbWs3b8DsHdVrQCuSfJ+4INJFlTVA8DraELXG9vnS5LsBLwLOG1tfEaSJGls\n06GHpt/mQAG/AkjyDGAr4OLhgqq6A7gC2KPdtDNNOOutuQFY2lOzO3DbcJhpXdS+1249Nde0YWbY\nImAm8JyemkvaMNNbs32SmQOcryRJWkPTKtAkCc3Q0Teq6rp281Y0oWN5X/nydh/ALOC+NuiMVrMV\nTc/PQ6rqQZrg1Fsz0vswwRpJkrQOTfmQU5+TgWcDL5jqhkiSpO6YNoEmyYnAy4A9q+qWnl3LgND0\nwvT2jMwCru6p2TjJjL5emlntvuGa/queNgS26KvZpa9ps3r2DX+dtZqaEc2fP5+ZMx85KjVv3jzm\nzZs31sskSVovDA0NMTQ09IhtK1euHNdrp0WgacPMnwIvrKqlvfuq6qYky2iuTPqftn4GzbyXk9qy\nq4AH2prPtjXbA9sCl7c1lwObJ9mpZx7NXJqwdEVPzVFJtuyZR7MPsBK4rqfm75Ns2A5ZDdfcUFVj\nfuoLFy5k9uzZ4/lIJEla74z0S/7ixYuZM2fOal875XNokpwMvBY4CLgryaz2sWlP2fHA+5K8PMlz\ngU8APwU+Dw9NEj4d+HCSFyWZA5wBXFpVV7Y119NM3j01yS5JXkBzufhQe4UTwJdogssn27Vm9gWO\nBU6sqvvbmrOB+4Azkjw7yWuAdwIfWhufjyRJWr3p0EPzVppJv//dt/1QmuBCVR2X5LE0a8ZsDnwd\neGlV3ddTPx94EDgH2AS4EDis75gHASfSXN20qq09YnhnVa1Ksj/wUeAymvVuzgSO7qm5I8k+NL1D\n3wZWAAuq6vSBzl6SJK2xKQ80VTWuXqKqWgAsGGP/vcDh7WO0mttp1pEZ631+Auy/mpprgReOVSNJ\nktadKR9ykiRJWlMGGkmS1HkGGkmS1HkGGkmS1HkGGkmS1HkGGkmS1HkGGkmS1HkGGkmS1HkGGkmS\n1HkGGkmS1HkGGkmS1HkGGkmS1HkGGkmS1HkDBZokf5lk08lujCRJ0iAG7aFZCCxLckqSXSezQZIk\nSRM1aKB5CvBm4KnApUmuTfLuJE+avKZJkiSNz0CBpqruq6r/qKr9gG2BTwJvBH6a5D+T7Jckk9lQ\nSZKk0azxpOCqugW4CPgqUMDOwBDwgyR7runxJUmSVmfgQJNkyyT/O8l3gUuBJwOvBJ4G/C7wOeAT\nk9JKSZKkMWw0yIuSfBZ4GXATcBrw8aq6tafkziTHAe9a8yZKkiSNbaBAA9wBvLiqvj5Gza3A7w14\nfEmSpHEbKNBU1cHjqCngxkGOL0mSNBGDLqy3MMlhI2w/LMmH1rxZkiRJ4zfopOC/AC4bYfs3gdcM\n3hxJkqSJGzTQbEkzj6bfynafJEnSOjNooLkR2HeE7fvSXPkkSZK0zgx6ldPxwPFJngh8pd02F/hr\n4D2T0TBJkqTxGvQqp1Pbu20fBXyg3fxT4J1VdcZkNU6SJGk8Bu2hoapOAE5IsjVwd1XdPnnNkiRJ\nGr+BA82w9l5OkiRJU2bQdWielORjSZYmuSfJfb2PyW6kJEnSWAbtoTkTeBbwL8AtNHfZliRJmhKD\nBpq9gL2q6urJbIwkSdIgBl2H5qfYKyNJkqaJQQPNfOCfkjx1MhsjSZI0iEGHnD4JPB64OckdwP29\nO6vqyWvaMEmSpPEaNND8zaS2QpIkaQ0MulLw6ZPdEEmSpEENOoeGJE9PsiDJJ5M8ud22T5IdJ695\nkiRJqzfownp7At8DXgi8Gnhcu2sOcMzkNE2SJGl8Bu2h+WdgQVXtDfSuDHwxsPsat0qSJGkCBg00\nzwPOGWH7L4AnTfRgSfZM8oUkP0uyKskr+vZ/rN3e+7igr2aTJCclWZHkziTnDA+F9dQ8IcmnkqxM\ncluS05Js1lezTZLzk9yVZFmS45Js0FfzvCSXJLk7yc1JjpzoOUuSpMkzaKBZCWw1wvbnAz8b4Hib\nAd8B3s7oC/Z9EZjVvu9WwLy+/ccD+wEH0Kxk/BTg3L6as4Edgblt7V7AKcM72+ByAc1k6d2Bg4FD\n6BlGS/J4YBFwEzAbOBJYkORN4z9dSZI0mQa9bPvTwAeT/DltAEmyG/Ah4KyJHqyqLgQubI+TUcru\nrapbR9qRZAbwBuDAqvpau+1QYEmSXavqynay8r7AnOFbNiQ5HDg/yXuqalm7fwdg76paAVyT5P3t\nuS6oqgeA1wGPAd7YPl+SZCfgXcBpEz13SZK05gbtoflb4EfAz2kmBF8HXAZ8Czh2cpr2W16UZHmS\n65OcnGSLnn1zaMLZxcMbquoGYCmwR7tpd+C2vvtPXUQTyHbrqbmmDTPDFgEzgef01FzShpnemu2T\nzFyjM5QkSQMZdB2ae4FDkxwDPJcm1Cyuqusns3E9vkgzfHQTzV2+/wm4IMkeVVU0Q1D3VdUdfa9b\nzsNDY1vRzPHpPY8Hk/yqr2b5CMcY3vfd9uuPxqhZObFTkyRJa2rQIScAquommpCxVlXVZ3qefi/J\nNcCNwIuAr67t95ckSdPbQIEmyb+Ptb+q3jJYc8anqm5KsgLYjibQLAM2TjKjr5dmVruP9mv/VU8b\nAlv01ezS93azevYNf521mpoRzZ8/n5kzHzkqNW/ePObN65/fLEnS+mdoaIihoaFHbFu5cnwDH4P2\n0Gzd9/wxNHNMHg9cMuAxx629y/cTgVvaTVcBD9BcvfTZtmZ7YFvg8rbmcmDzJDv1zKOZCwS4oqfm\nqCRb9syj2YdmGOm6npq/T7JhVT3YU3NDVY35qS9cuJDZs2cPcsqSJD3qjfRL/uLFi5kzZ85qXzvo\nHJqX929LshHwbzz8g3/c2rVgtqMJFwDPTPJ84Fft42iaOTTL2rp/Br5PMxmXqrojyenAh5PcBtwJ\nfAS4tKqubGuuT7IIODXJ24CNgROAofYKJ4Avte3/ZJL30gS3Y4ETq2r4juJnA38HnJHkn2nmEL0T\nOGKi5y1JkibHwPdy6tde9fMvNOuyTNTOwNU0PS1Fc/n3YuADwIM0C/l9HrgBOJXmaqq9ekIGwHzg\nPJoF//6b5gqsA/re5yDgepqrm86j6U36q55zWAXs377nZcAngDNpAtVwzR00PTJPB77dnvMCb9gp\nSdLUWaNJwSN4Bs3w04S0a8eMFa5eMo5j3Asc3j5Gq7mdZh2ZsY7zE5pQM1bNtTT3sZIkSdPAoJOC\nj+vfRDM88woGWFhPkiRpTQzaQ7NH3/NVwK3A39AMCUmSJK0zg04K3nOyGyJJkjSoSZsULEmSNFUG\nnUPzLUa/K/YjVNWug7yHJEnSeA06h+arNJc7f5+HF67bHdgeOAW4d82bJkmSND6DBprNgZOq6qje\njUn+AZhVVW9a45ZJkiSN06BzaF4NfGyE7WcCfzFwayRJkgYwaKC5l2aIqd/uONwkSZLWsUGHnD4C\nnJJkJ+DKdttuwJuBf5qMhkmSJI3XoOvQ/EOSm2huyDg8X2YJ8JaqOnuyGidJkjQeA9/LqQ0uhhdJ\nkjTlBl5YL8mMJIckOSbJE9ptz0+y9eQ1T5IkafUGXVjvD4CLgN8A29Bc3XQb8Brgd4GDJ6l9kiRJ\nqzVoD81CmuGmZwH39Gw/H9hrTRslSZI0EYMGml2Ak6uq//YHPwMccpIkSevUoIHmfuBxI2zfDlgx\neHMkSZImbtBA81/A+5MMz8GpJL8LfBD4z0lpmSRJ0jgNGmjeDWwBLAN+B/gK8COa+TRHjfE6SZKk\nSTfownq3AXsneSHwfJrhp8XAohHm1UiSJK1VEw40SR4DnAe8o6q+Bnxt0lslSZI0ARMecqqq+4E5\ngD0xkiRpWhh0Ds2ngEMnsyGSJEmDGvReTgW8I8mLgW8Ddz1iZ9Vfr2nDJEmSxmvQQDMH+J/2z8/r\n2+dQlCQRjGfAAAAO2klEQVRJWqcmFGiSPBO4qar2XEvtkSRJmrCJzqH5AfCk4SdJPp1k1uQ2SZIk\naWImGmjS9/xlwGaT1BZJkqSBDHqVkyRJ0rQx0UBT/PakXycBS5KkKTXRq5wCnJnk3vb5psC/Jem/\nbPtVk9E4SZKk8ZhooPl43/OzJqshkiRJg5pQoKkqVweWJEnTjpOCJUlS5xloJElS5xloJElS5xlo\nJElS5xloJElS5xloJElS5xloJElS5xloJElS502LQJNkzyRfSPKzJKuSvGKEmmOS/DzJb5J8Ocl2\nffs3SXJSkhVJ7kxyTpIn99U8IcmnkqxMcluS05Js1lezTZLzk9yVZFmS45Js0FfzvCSXJLk7yc1J\njpzMz0OSJE3MtAg0wGbAd4C3M8LNLpO8F3gH8BZgV+AuYFGSjXvKjgf2Aw4A9gKeApzbd6izgR2B\nuW3tXsApPe+zAXABzQrKuwMHA4cAx/TUPB5YBNwEzAaOBBYkedMgJy5JktbcRO/ltFZU1YXAhQBJ\nMkLJEcCxVXVeW/N6YDnwSuAzSWYAbwAOrKqvtTWHAkuS7FpVVybZEdgXmFNVV7c1hwPnJ3lPVS1r\n9+8A7F1VK4Brkrwf+GCSBVX1APA64DHAG9vnS5LsBLwLOG0tfDySJGk1pksPzaiSPAPYCrh4eFtV\n3QFcAezRbtqZJpz11twALO2p2R24bTjMtC6i6RHarafmmjbMDFsEzASe01NzSRtmemu2TzJzwNOU\nJElrYNoHGpowUzQ9Mr2Wt/sAZgH3tUFntJqtgF/07qyqB4Ff9dWM9D5MsEaSJK1D02LIaX0xf/58\nZs58ZCfOvHnzmDdv3hS1SJKk6WNoaIihoaFHbFu5cuW4XtuFQLMMCE0vTG/PyCzg6p6ajZPM6Oul\nmdXuG67pv+ppQ2CLvppd+t5/Vs++4a+zVlMzooULFzJ79uyxSiRJWm+N9Ev+4sWLmTNnzmpfO+2H\nnKrqJpqgMHd4WzsJeDfgsnbTVcADfTXbA9sCl7ebLgc2byfwDptLE5au6Kl5bpIte2r2AVYC1/XU\n7NWGod6aG6pqfDFSkiRNqmkRaJJsluT5Sf6w3fTM9vk27fPjgfcleXmS5wKfAH4KfB4emiR8OvDh\nJC9KMgc4A7i0qq5sa66nmbx7apJdkrwAOAEYaq9wAvgSTXD5ZLvWzL7AscCJVXV/W3M2cB9wRpJn\nJ3kN8E7gQ2vn05EkSaszXYacdga+SjP5t3g4HHwceENVHZfksTRrxmwOfB14aVXd13OM+cCDwDnA\nJjSXgR/W9z4HASfSXN20qq09YnhnVa1Ksj/wUZren7uAM4Gje2ruSLIPcBLwbWAFsKCqTl+zj0CS\nJA1qWgSadu2YMXuLqmoBsGCM/fcCh7eP0Wpup1lHZqz3+Qmw/2pqrgVeOFaNJElad6bFkJMkSdKa\nMNBIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BI\nkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTO\nM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BI\nkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTOM9BIkqTO\nM9BIkqTO60SgSXJ0klV9j+v6ao5J8vMkv0ny5STb9e3fJMlJSVYkuTPJOUme3FfzhCSfSrIyyW1J\nTkuyWV/NNknOT3JXkmVJjkvSic9RkqRHqy79IL4WmAVs1T7+eHhHkvcC7wDeAuwK3AUsSrJxz+uP\nB/YDDgD2Ap4CnNv3HmcDOwJz29q9gFN63mcD4AJgI2B34GDgEOCYyTlFSZI0iI2mugET8EBV3TrK\nviOAY6vqPIAkrweWA68EPpNkBvAG4MCq+lpbcyiwJMmuVXVlkh2BfYE5VXV1W3M4cH6S91TVsnb/\nDsDeVbUCuCbJ+4EPJllQVQ+srZOXJEmj61IPze8l+VmSG5OclWQbgCTPoOmxuXi4sKruAK4A9mg3\n7UwT3nprbgCW9tTsDtw2HGZaFwEF7NZTc00bZoYtAmYCz5mUs5QkSRPWlUDzTZqhnX2BtwLPAC5p\n57dsRRM6lve9Znm7D5qhqvvaoDNazVbAL3p3VtWDwK/6akZ6H3pqJEnSOtaJIaeqWtTz9NokVwI3\nA68Grp+aVkmSpOmiE4GmX1WtTPJ9YDvgv4HQ9ML09p7MAoaHj5YBGyeZ0ddLM6vdN1zTf9XThsAW\nfTW79DVnVs++Mc2fP5+ZM2c+Ytu8efOYN2/e6l4qSdKj3tDQEENDQ4/YtnLlynG9tpOBJsnjaMLM\nx6vqpiTLaK5M+p92/wyaeS8ntS+5CnigrflsW7M9sC1weVtzObB5kp165tHMpQlLV/TUHJVky555\nNPsAK4FHXEY+koULFzJ79uzBTlqSpEe5kX7JX7x4MXPmzFntazsRaJL8C/BfNMNMvwt8ALgf+H9t\nyfHA+5L8EPgxcCzwU+Dz0EwSTnI68OEktwF3Ah8BLq2qK9ua65MsAk5N8jZgY+AEYKi9wgngSzTB\n5ZPtpeJbt+91YlXdvxY/AkmSNIZOBBrgqTRrxDwRuBX4BrB7Vf0SoKqOS/JYmjVjNge+Dry0qu7r\nOcZ84EHgHGAT4ELgsL73OQg4kebqplVt7RHDO6tqVZL9gY8Cl9Gsd3MmcPQknqskSZqgTgSaqlrt\nJJOqWgAsGGP/vcDh7WO0mtuB163mfX4C7L+69kiSpHWnK5dtS5IkjcpAI0mSOs9AI0mSOs9AI0mS\nOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9A\nI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mS\nOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9A\nI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AI0mSOs9AM6Ak\nhyW5KcndSb6ZZJepbpPWpaGpboCkMQwN+W90fWOgGUCS1wAfAo4GdgK+CyxKsuWUNkzrkP9ZStOZ\ngWb9Y6AZzHzglKr6RFVdD7wV+A3whqltliRJ6ycDzQQleQwwB7h4eFtVFXARsMdUtUuSpPXZRlPd\ngA7aEtgQWN63fTmw/bpvjiSNz9KlS1mxYsVUN2OdWLlyJYsXL57qZqx1W265Jdtuu+1UN2NaMNCs\nG5sCLFmyZKrbsVY9fH4XAI/uc4WfAp+a6kasZTcBj/6/t+uLW265hVe96s+57757prop68ycOXOm\nuglr3cYbb8p//uc5bL311lPdlLWm5/+gTceqSzNaovFqh5x+AxxQVV/o2X4mMLOq/myE1xzEo/+n\nnyRJa9Nrq+rs0XbaQzNBVXV/kquAucAXAJKkff6RUV62CHgt8GNg/fn1SJKkNbcp8HSan6Wjsodm\nAEleDZxJc3XTlTRXPf05sENV3TqFTZMkab1kD80Aquoz7ZozxwCzgO8A+xpmJEmaGvbQSJKkznMd\nGkmS1HkGGkmS1HnOoZFWo50v9QaalaC3ajcvAy4DznTulCRNPXtopDG0d1H/PvBOYCVwSftY2W67\nPsnOU9dCSWNJsk2SM6a6HVr7nBQsjSHJN2nupv7W6vvH0q4/9G/A86rK+3hJ01CS5wOLq2rDqW6L\n1i6HnKSxPR84pD/MQHNT0iQLgavXfbMkASR5xWpKnrlOGqIpZ6CRxrYM2BW4fpT9u/LbNyqVtO58\nDiggY9Q4FLEeMNBIY/tX4N+TzAEu5uHwMovmdhdvBt4zRW2TBLcAb6+qz4+0M8kfAlet2yZpKhho\npDFU1UlJVtDc3uLtwPA4/IM0/0keUlWfmar2SeIqYA4wYqBh9b03epRwUrA0Tu2d1rdsn66oqvun\nsj2SIMmewGZVdeEo+zcDdq6qr63blmldM9BIkqTOcx0aSZLUeQYaSZLUeQYaSZLUeQYaSZLUeQYa\nSZLUeQYaSeuFJKvGsUy+pI4y0Eh6VEgyK8kJSW5Mck+Sm5N8IcmfTHXbJK19rhQsqfOSPA24DPgV\n8G7gWuAxwEuAE4FnT13rJK0L9tBIejT4KM3tKHapqs9V1Q+raklVLQR2H+kFST6Y5IYkd7W9Osck\n2bBn//OSfCXJHUlWJvlWktntvm3b3p9fJfl1kmuSvGSdnKmkEdlDI6nTkjwB2Bf426q6p39/Vd0x\nykvvAF5Pc3PD5wKnttv+td3/KWAx8FfAKuAPgeHbXZxM8//nHwO/oekB+vUknI6kARloJHXddjQ3\nH7xhIi+qqn/sebo0yYeA1/BwoNkWOK6qftA+v7GnfhvgnKq6rn3+44k2WtLkMtBI6rqB7qSc5DXA\n4cCzgMfR/H+4sqfkw8DpSV4PXAT8R1X9qN33EeCjSfZt951bVdcM2H5Jk8A5NJK67gdAATuM9wVJ\n9gDOAs4D9qMZTvoHYOPhmqr6AM1Q0nnAnwDfS/Kn7b7TgWcAnwD+APhWksMm42QkDca7bUvqvCQX\n0ASL7avq7r59M6tqZZJVwCur6gtJ3gW8rap+r6fuNOBVVbXFKO9xNvDYqnrlCPv+EXhZVf3hJJ6W\npAmwh0bSo8FhwIbAlUlelWS7JDskeSfN5dz9fgBsm+Q1SZ7Z1j0UVJJs2q5p88L2iqYXALsA17X7\nFybZJ8nT2yuf9h7eJ2lqOIdGUudV1U1tsPg/NJN6twZuBf4HeNdwWU/9fyVZCJwAbAKcDxwDLGhL\nHgSeCHwcmAWsAM7t2b8hzfo2T6W5MuqLPe8jaQo45CRJkjrPISdJktR5BhpJktR5BhpJktR5BhpJ\nktR5BhpJktR5BhpJktR5BhpJktR5BhpJktR5BhpJktR5BhpJktR5BhpJktR5BhpJktR5/x/2ZHmW\nFh6aXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2163655cf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_classes = pd.value_counts(data['Class'], sort = True).sort_index()\n",
    "count_classes.plot(kind = 'bar')\n",
    "plt.title(\"Fraud class histogram\")\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "      <th>normAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "      <td>0.244964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.342475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>1.160686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>0.140534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.073403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10     ...           V21       V22       V23  \\\n",
       "0  0.098698  0.363787  0.090794     ...     -0.018307  0.277838 -0.110474   \n",
       "1  0.085102 -0.255425 -0.166974     ...     -0.225775 -0.638672  0.101288   \n",
       "2  0.247676 -1.514654  0.207643     ...      0.247998  0.771679  0.909412   \n",
       "3  0.377436 -1.387024 -0.054952     ...     -0.108300  0.005274 -0.190321   \n",
       "4 -0.270533  0.817739  0.753074     ...     -0.009431  0.798278 -0.137458   \n",
       "\n",
       "        V24       V25       V26       V27       V28  Class  normAmount  \n",
       "0  0.066928  0.128539 -0.189115  0.133558 -0.021053      0    0.244964  \n",
       "1 -0.339846  0.167170  0.125895 -0.008983  0.014724      0   -0.342475  \n",
       "2 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0    1.160686  \n",
       "3 -1.175575  0.647376 -0.221929  0.062723  0.061458      0    0.140534  \n",
       "4  0.141267 -0.206010  0.502292  0.219422  0.215153      0   -0.073403  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "data['normAmount'] = StandardScaler().fit_transform(data['Amount'].reshape(-1, 1))\n",
    "data = data.drop(['Time','Amount'],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of normal transactions:  0.5\n",
      "Percentage of fraud transactions:  0.5\n",
      "Total number of transactions in resampled data:  984\n"
     ]
    }
   ],
   "source": [
    "X = data.ix[:, data.columns != 'Class']\n",
    "y = data.ix[:, data.columns == 'Class']\n",
    "\n",
    "# Number of data points in the minority class\n",
    "number_records_fraud = len(data[data.Class == 1])\n",
    "fraud_indices = np.array(data[data.Class == 1].index)\n",
    "\n",
    "# Picking the indices of the normal classes\n",
    "normal_indices = data[data.Class == 0].index\n",
    "\n",
    "# Out of the indices we picked, randomly select \"x\" number (number_records_fraud)\n",
    "random_normal_indices = np.random.choice(normal_indices, number_records_fraud, replace = False)\n",
    "random_normal_indices = np.array(random_normal_indices)\n",
    "\n",
    "# Appending the 2 indices\n",
    "under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])\n",
    "\n",
    "# Under sample dataset\n",
    "under_sample_data = data.iloc[under_sample_indices,:]\n",
    "\n",
    "X_undersample = under_sample_data.ix[:, under_sample_data.columns != 'Class']\n",
    "y_undersample = under_sample_data.ix[:, under_sample_data.columns == 'Class']\n",
    "\n",
    "# Showing ratio\n",
    "print(\"Percentage of normal transactions: \", len(under_sample_data[under_sample_data.Class == 0])/len(under_sample_data))\n",
    "print(\"Percentage of fraud transactions: \", len(under_sample_data[under_sample_data.Class == 1])/len(under_sample_data))\n",
    "print(\"Total number of transactions in resampled data: \", len(under_sample_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number transactions train dataset:  199364\n",
      "Number transactions test dataset:  85443\n",
      "Total number of transactions:  284807\n",
      "\n",
      "Number transactions train dataset:  688\n",
      "Number transactions test dataset:  296\n",
      "Total number of transactions:  984\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# Whole dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3, random_state = 0)\n",
    "\n",
    "print(\"Number transactions train dataset: \", len(X_train))\n",
    "print(\"Number transactions test dataset: \", len(X_test))\n",
    "print(\"Total number of transactions: \", len(X_train)+len(X_test))\n",
    "\n",
    "# Undersampled dataset\n",
    "X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = train_test_split(X_undersample\n",
    "                                                                                                   ,y_undersample\n",
    "                                                                                                   ,test_size = 0.3\n",
    "                                                                                                   ,random_state = 0)\n",
    "print(\"\")\n",
    "print(\"Number transactions train dataset: \", len(X_train_undersample))\n",
    "print(\"Number transactions test dataset: \", len(X_test_undersample))\n",
    "print(\"Total number of transactions: \", len(X_train_undersample)+len(X_test_undersample))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Recall = TP/(TP+FN)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import KFold, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix,recall_score,classification_report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def printing_Kfold_scores(x_train_data,y_train_data):\n",
    "    fold = KFold(len(y_train_data),5,shuffle=False) \n",
    "\n",
    "    # Different C parameters\n",
    "    c_param_range = [0.01,0.1,1,10,100]\n",
    "\n",
    "    results_table = pd.DataFrame(index = range(len(c_param_range),2), columns = ['C_parameter','Mean recall score'])\n",
    "    results_table['C_parameter'] = c_param_range\n",
    "\n",
    "    # the k-fold will give 2 lists: train_indices = indices[0], test_indices = indices[1]\n",
    "    j = 0\n",
    "    for c_param in c_param_range:\n",
    "        print('-------------------------------------------')\n",
    "        print('C parameter: ', c_param)\n",
    "        print('-------------------------------------------')\n",
    "        print('')\n",
    "\n",
    "        recall_accs = []\n",
    "        for iteration, indices in enumerate(fold,start=1):\n",
    "\n",
    "            # Call the logistic regression model with a certain C parameter\n",
    "            lr = LogisticRegression(C = c_param, penalty = 'l1')\n",
    "\n",
    "            # Use the training data to fit the model. In this case, we use the portion of the fold to train the model\n",
    "            # with indices[0]. We then predict on the portion assigned as the 'test cross validation' with indices[1]\n",
    "            lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel())\n",
    "\n",
    "            # Predict values using the test indices in the training data\n",
    "            y_pred_undersample = lr.predict(x_train_data.iloc[indices[1],:].values)\n",
    "\n",
    "            # Calculate the recall score and append it to a list for recall scores representing the current c_parameter\n",
    "            recall_acc = recall_score(y_train_data.iloc[indices[1],:].values,y_pred_undersample)\n",
    "            recall_accs.append(recall_acc)\n",
    "            print('Iteration ', iteration,': recall score = ', recall_acc)\n",
    "\n",
    "        # The mean value of those recall scores is the metric we want to save and get hold of.\n",
    "        results_table.ix[j,'Mean recall score'] = np.mean(recall_accs)\n",
    "        j += 1\n",
    "        print('')\n",
    "        print('Mean recall score ', np.mean(recall_accs))\n",
    "        print('')\n",
    "\n",
    "    best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']\n",
    "    \n",
    "    # Finally, we can check which C parameter is the best amongst the chosen.\n",
    "    print('*********************************************************************************')\n",
    "    print('Best model to choose from cross validation is with C parameter = ', best_c)\n",
    "    print('*********************************************************************************')\n",
    "    \n",
    "    return best_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "C parameter:  0.01\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.958904109589\n",
      "Iteration  2 : recall score =  0.917808219178\n",
      "Iteration  3 : recall score =  1.0\n",
      "Iteration  4 : recall score =  0.972972972973\n",
      "Iteration  5 : recall score =  0.954545454545\n",
      "\n",
      "Mean recall score  0.960846151257\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  0.1\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.835616438356\n",
      "Iteration  2 : recall score =  0.86301369863\n",
      "Iteration  3 : recall score =  0.915254237288\n",
      "Iteration  4 : recall score =  0.932432432432\n",
      "Iteration  5 : recall score =  0.878787878788\n",
      "\n",
      "Mean recall score  0.885020937099\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  1\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.835616438356\n",
      "Iteration  2 : recall score =  0.86301369863\n",
      "Iteration  3 : recall score =  0.966101694915\n",
      "Iteration  4 : recall score =  0.945945945946\n",
      "Iteration  5 : recall score =  0.893939393939\n",
      "\n",
      "Mean recall score  0.900923434357\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  10\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.849315068493\n",
      "Iteration  2 : recall score =  0.86301369863\n",
      "Iteration  3 : recall score =  0.966101694915\n",
      "Iteration  4 : recall score =  0.959459459459\n",
      "Iteration  5 : recall score =  0.893939393939\n",
      "\n",
      "Mean recall score  0.906365863087\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  100\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.86301369863\n",
      "Iteration  2 : recall score =  0.86301369863\n",
      "Iteration  3 : recall score =  0.966101694915\n",
      "Iteration  4 : recall score =  0.959459459459\n",
      "Iteration  5 : recall score =  0.893939393939\n",
      "\n",
      "Mean recall score  0.909105589115\n",
      "\n",
      "*********************************************************************************\n",
      "Best model to choose from cross validation is with C parameter =  0.01\n",
      "*********************************************************************************\n"
     ]
    }
   ],
   "source": [
    "best_c = printing_Kfold_scores(X_train_undersample,y_train_undersample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=0)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the testing dataset:  0.931972789116\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAc4AAAGbCAYAAABNpXD0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3XmcXvP5//HXe7KIpJZYsiCxxfYriZ3UvlQoVYrSqrUo\ntVVr/5ZYSlFUtbSKWlotsXwJQhDL11L7kiJCJPZsiCSykly/P86ZuGcymbnPPefOPffc76fHeWTu\nz9muM4m55jqfz/kcRQRmZmZWnLpKB2BmZlZNnDjNzMwycOI0MzPLwInTzMwsAydOMzOzDJw4zczM\nMnDiNDMzy8CJ08zMLAMnTjMzswycOK3qSOon6SFJX0iaJ2nPnI+/qqT5kg7O87jtgaT3JP290nGY\nVZITp5VE0hqSrpH0rqRZkqZKekrSCZK6lPn0NwPfBs4EDgJeLMM52vVclJLWkzRYUt+Mu86nnX9v\nzFoiz1VrWUnaHRgCzCZJYq8DnYGtgX2AGyPi6DKduwswEzg/IgaX4xzpeToDX0U7/R9E0j7A7cD2\nEfF/GfbrBMyPiHllC86sjetY6QCsukhaDfg3MA7YMSImFaz+i6SzgN3LGEKP9M+pZTwHETG3nMdv\nA0SGylFSl4iYHRFflTEms6rgW7WW1WlAN+BnjZImABExNiL+VP9ZUgdJZ0kaI2m2pHGSLkgrOgq2\ne0/SUElbSXouvf37rqSDCrYZDLxH8gP/0rQfcmy67kZJ4xrHI+kcSfMbtX1X0pOSpkiaLuktSRcU\nrG+yj1PSjul+X6b73i1p3abOJ2nNNKYpaV/s34u5hS3pcUkjJW2Qfj1D0jtphYik7SQ9K2lmGvdO\njfbvK+nqdN1MSZ9KGiJp1YJtDiG5YwDweBrvPEnbNvq72EXSC5JmAUcVrPt7wbEelTRJ0goFbZ0k\n/TeNe8mWrtms2jhxWlZ7AGMj4rkit78eOJekH/KXwOPAGSRVa6EA1iK5ffgQ8Cvgc+AGSeul29yZ\nHkPAv4Cfpp/r92+qgmrQLun/AfcCnYCz0vPcA3ynuYuQtDPwILACMBi4LN3nqUb9hPXnGkLyC8bp\nwG3AIel+LQlguTTGZ4FTSG6J/1vSj0i+b/fxzS8wt0vqVrD/ZsCW6XbHA38BdgIeK0jcTwBXpl//\nluT7eBAwqiCGdUm+xw8BJwCvNrq+eocDXYC/FrSdB6wHHBoRs4q4ZrPqEhFevBS1AEuRDA65q8jt\n+6fb/7VR+yXAPGC7grZxadt3CtpWAGYBlxS0rZoe81eNjnkDSUJvHMNgYF7B5xPT83RvJu76cxxc\n0PYKMB5YpqBtA+Br4IZG55sP/K3RMe8EJhXxPXssje9HBW1rp8f8Cti0oP27TcS5RBPH3Dzd7sCC\ntn3S82zbxPb1fxc7L2Ld3xu1HZke/8fAFmmcl1b636sXL+VaXHFaFkunf04vcvvvkVQof2jUfhlJ\n1di4L/TNiHim/kNEfAqMBtbIHuoifZH+ubckFbODpF7AAJIEuaBvNSL+CzxMcp2FArimUduTwPKS\nvlXEKb+MiPpbqUTE22ncoyKicARxfdW/RsG2cwri7ihpOWBsuv/GRZy73riIeKSYDSPiWpJq/M8k\ng8XeAf4nw7nMqooTp2UxLf1zqSK3r6/cxhQ2RsREkh/kqzba/oMmjjEF6J4hxpbcBjwNXAtMlPRv\nSfu1kETr43y7iXWjgBWa6MtrfC1T0j+LuZaPmmibCnxY2BAR9X8fC44pqYuk8yR9AMwBPgUmAcuk\nS7EW6i9uwRFAV6AfcFhhAjdrb5w4rWgRMR34BFg/665FbreoRxyKqQwXdY4ODTZKRoZuC+xMUh1t\nQJJMHyq2Ai1Sa65lUfsWc8w/k/Qh3wrsR3I7d2eS/uIs/79n7ZvcAVgi/XqDjPuaVRUnTsvqPmBN\nSVsUse37JP/G1ipslNQDWDZdn5cp6TEbW62pjSPisYg4OSLWJ7mtuCPJD/+m1Me5ThPr1gU+jbYz\nCKb+OdpTI+KuiBhBUmE3/t7k9nyqpN4kg42Gk/z7uExSn7yOb9bWOHFaVpeQTEBwXZoAG0gfwzgh\n/TiMpBr6ZaPNfk3yg/v+HON6F1hG0oJqOP2Bvlej+Jq6VfpaGucSTawjIiaQjCo9RFJ9Py/puXYh\n3+torXks/P/1CTSqvIEZJNfc1C8bWV2bHutw4OckA6auz+G4Zm2SJ0CwTCJirKSfkNwKHCWpcOag\nrYB9SUa4EhEjJd0EHJUmrCdIRl0eTDIy94kcQ7sVuBi4W9KVJI9qHE0yuKhwUMzZ6fOK95NUkj2B\nY0j6JJ9q5vinkPwi8Kyk60n6844jqXTPzfE6Wus+4CBJ04A3gYEkj6N82mi7V0mS7GmSliXpDx2R\nDsgqmqTDSAZHHRwR49O244F/SjomIv7Sqqsxa4OcOC2ziLhXUn+SZLInSYKaS5JATwb+VrD5z0iq\nwUNJqr8JwAUkz/o1OCyLvn3YuH2hbSPic0l7AZeTJNBxJM9Qrk3DxHkPyWCfw0ged/mU5NnSc9I+\n3CbPGREjJO1KkiTPJXnk4nHg9IjI85bzQucuaCum/QSSiu8nJM9XPkXSxzm8cLuImCjp5yT9odeR\nVKQ7APXT7zX3dxEAklYm+X7fExH/LDj2v9IJGy6WNKwM3x+zivJctWZmZhm4j9PMzCwDJ04zM7MM\nnDjNzMwycOI0MzPLwInTzMwsg6p7HEXS8sAgkvcyzq5sNGZmbVIXklmzhkfEZ4vrpOkr9lZoccNF\n+zQimpqzGknbkDwCtwnQG9grIoYWrB8MHAD0IXk87iXgfyLi+YJtHge2LThsANdExC+yBFl1iZMk\nad5S6SDMzKrAgSTvVS07SX2p6/g+879uzWFmSlpvEcmzG8nEHdcDdzWxfjRwLMnbgJYkedfuQ5LW\nLPjlIUieMz+Lb+Z4npk1yGpMnO8BdFxvX+q6rljhUKrfV2OG0alf47diWVb3XnZIpUNoNwafeTLn\nXnhppcOoau+8/RbHHXUopD8vF5MVmP81nfrujLosl3nnmP05X33wSFeSinWhxBkRD5K8vo6mXsgQ\nEbcWfpb0K5IJWPqTvOe23syImJw5wALVmDhnA9R1XZG6pVaqdCxVTx27+PuYg/4bblTpENqNpZde\nxt/P/Cz27iwtuTx1XReaxrpF83N8OZGkTiTzJn9BMhd1oQMlHUQyi9m9wPlZX9JQjYnTzMzaKilZ\nStmv1afW7iTzVncleQXidyPi84JNbiGZo/oTkkr0EpJpOffNch4nTjMzy4/qkqWU/VrvUWAAye3e\nI4HbJW1e//KCiLiuYNs3JI0HRkhaPSKKfnm7E6eZmeWrhepx3mdvMe/z0Q3aYt6cVp82veU6Nl2e\nl/Q2ST/nxYvY5XmSQUL9SF4MURQnzhrXoUf/Sodg1sBe++5f6RCszDosvy4dll+3Qdv8GROZOyr3\nAcB1LOI9u6mNSEbajs9yUCfOGtehpxOntS17O3FWtzLdqpXUjaQyrC9n15A0APgc+Az4H2AoSRJc\ngeR9uSsBt6f7r0Hyur1h6fYDSF6L90REvJ4lVCdOMzPLT/kGB21K8lhJ/TthL0vbbyJ5Gf26wMEk\nSfMz4AVg64gYlW43l+TdtCeSPBP6IUlSvSBrqE6cZmaWH6nEirP5xBkRT9D8NLH7tLD/R8D2meNq\ngueqNTMzy8AVp5mZ5aeCz3EuLk6cZmaWoxIHB1XRDVAnTjMzy48rTjMzswzKNDioLame2tjMzKwN\ncMVpZmb58a1aMzOzDCo7yfti4cRpZmY5KrGPk+qpOKsnxZuZmbUBrjjNzCw/dUqWUvarEk6cZmaW\nH/dxmpmZZSBKHFWbeyRl48RpZmY5av9T7lVPpGZmZm2AK04zM8uPJ0AwMzPLoAbmqnXiNDOz/NRA\nxek+TjMzswxccZqZWX78HKeZmVkWJd6qraIHOZ04zcwsPx4cZGZmloEHB5mZmVkhV5xmZpYfDw4y\nMzPLwH2cZmZmWbT/UbXVUxubmZm1AU6cZmaWn/o+zlKW5g4rbSNpqKSPJc2XtGfBuo6SLpY0UtKX\n6TY3Serd6BhLSLpK0qeSpku6Q1KPrJfoxGlmZvmpfxyllKV53YBXgV8A0WhdV2BD4FxgI2BvYB3g\nnkbbXQHsDuwDbAusBNyZ9RLdx2lmZvkp0+CgiHgQeDDZtOHGETENGNTwcDoOeE7SKhHxkaSlgcOB\nAyLiiXSbw4BRkjaPiOeLDdUVp5mZ5ad8FWdWy5JUpl+knzchKRZH1G8QEaOBD4CBWQ7sxGlmZu2K\npCWAi4B/RcSXaXMvYG5anRaamK4rmm/VmplZboRQCdWjcnocRVJH4HaSavMXuRy0ESdOMzPLjdRy\n4vz6w2eZ92HDLsX4amYe565Pmn2AHQuqTYAJQGdJSzeqOnum64rmxGlmZvkRLc5l0LHvlnTsu2WD\ntvlT3mf2iHNLP+03SXMNYIeImNJok5eAr4GdgP9N91kH6Av8J8u5nDjNzKzNk9QN6Mc3aXkNSQOA\nz4HxJI+VbAjsAXSS1DPd7vOI+Coipkm6Hrhc0hRgOnAl8HSWEbXgxGlmZnkSJfVxFtHFuSnwGEnf\nZQCXpe03kTy/+f20/dWCIwawA/B/adtJwDzgDmAJksdbjs0aqhOnmZnlppg+zkXt15z02cvmngRp\n8SmRiJgDHJ8uJXPiNDOz3FR6VO3i4Oc4zczMMnDFaWZmuSnXrdq2xInTzMzyU8TjKIvcr0o4cZqZ\nWW5ccZqZmWVRvsdR2gwPDjIzM8vAFaeZmeWmFh5HceI0M7PcuI/TzMwsixoYVes+TjMzswxccZqZ\nWW58q9bMzCwDJ04zM7OMqikJlsKJ08zM8uPBQWZmZlbIFaeZmeXGfZxmZmYZOHGamZllUAuJ032c\nZmZmGbjiNDOz3NRCxenEaWZm+aqeHFgSJ04zM8uPX2Rt1WarDfpw+29/xLtDTmDGiP9h9++stWBd\nhzrx26N25PnrjmTysFN5d8gJXHv6nvRa7lsNjrFa72W59dx9ef+uk5hw78ncfNberLhs18V9KdZO\nXXn5Jey241as1WcFNlirD4cduB/vjnl7oe0uueBcNlx3NVbvvSz777Ub48aOqUC0llX9rdpSlmrh\nxNnOdO3SiZHvTuTEPz5IRCy0rn+/nlx485NseeS17H/2HazdZzlu/+1+C7ZZcomO3Pf7nzA/gkEn\n/YMdjr+RJTp14M4L91/cl2Lt1HP/eYrDj/oF9z/yJEPufoCvv/6KA/beg1mzZi3Y5s9XXMoN1/6F\n319xFQ+MeJolu3bjxz/cg7lz51YwcrOEb9W2Mw+/MJaHXxgLLHy7ZPrMuex56r8LWqZw0h+H839X\nH8bKKyzFx59OZ+D6fejbcxk2P+JaZs7+CoAjLhrK+KEns91Gq/HEK+8tpiux9uqW24c2+HzF1dex\nQb9VGPnqy2wxcCsArvvrn/nlKWfw3V13B+BPf/07/dfuw4P3D2XPvfdd7DFb8WphcJArzhq3zLe6\nEMAXX84GYIlOHYiAuV/NW7DNnK/mMT+C72zQp0JRWns2beoXSGLZ7ssB8MF745g0cQLbbLfjgm2W\nWnppNtpkc158/tlKhWnFUiuWKtFmEqekYyWNkzRL0rOSNqt0TO1d504dOP+oHbhtxOvMSKvL59/8\nmBmz53Lhz3eiS+eOdO3SiYuO3pk6aaG+ULPWigjOPuNkNt9yK9ZZdz0AJk2aiCRW7NGjwbYr9ujB\n5EkTKxGmZSBK7OOsoszZJhKnpP2By4DBwEbAa8BwSStUNLB2rEOduGXwPhBw4h8eWND+2bRZHHjO\nXew2cC0+HXYq44eezFLdluDVMROY36jP1Ky1Tv/18bz91ij++vd/VDoUa+MkbSNpqKSPJc2XtGcT\n25wn6RNJMyU9LKlfo/WPp/vWL/MkXZ01lrbSx3kScE1E3Awg6Whgd+Bw4JJKBtYedagT/zpnH1bp\nsTS7/eqfC6rNeo+9PI4NDrqa7kt14et585k+cy5j7ziR9z6ZUqGIrT0685QTGfHQcO5+YAQ9e/Ve\n0N6jR08igsmTJrFij54L2idPmsT6/QdUIlTLoIx9nN2AV4Hrgbua2P804DjgYOA94LckBdh6EVE/\nqiyAvwFn8c3N4ZlZY614xSmpE7AJMKK+LZLhoI8AAysVV3tVnzRXW6k73/v1Pxf0bTZlyvTZTJ85\nl+02Wo0Vl+3Gfc8s/MiAWSnOPOVEhg+7jzvvG84qffo2WNd3tdXp0bMXTz7x6IK26dOm8cpLz7PZ\nFv6R0OaptEdSWrpTGxEPRsTZEXEPTW99InB+RNwXEa+TJNCVgL0abTczIiZHxKR0+TLrJbaFinMF\noAPQuPNiIrDO4g+nunXt0ok1V+6+oL9g9d7d2WDNHkyZNpvxn03n3+fty4A1e/HDM2+lU8cO9Oje\nDYDPp83i63nzAfjpoP6M/uBTJn8xky2/vQq/P24Xrrz9Wd792BWntd7pvz6eu+8Ywo3/vpOuXbst\n6Ldcaull6NKlCwBHHnMcf7z0IlZfY0369F2Niy84h94rrcyg732/kqFbESoxqlbS6kAvGhZg0yQ9\nR1KADSnY/EBJBwETgHtJku0sMmgLidNytPE6vRl++UFEBBHBRcfsDMA/HxrJhTc9ye4D1yYieO7a\nI4HkH2tEMOhX/+TpkR8AsHaf5TnvyB3ovtSSvD/hCy76x5NcdecLFbsma19u/vu1SGKfPb7boP0P\nV13Lj378UwCOPfFkZs2cxaknHcfUqV+wxcCtuOWOoXTu3LkSIVsWpY6Qbd3YoF4kt2GbKsB6FXy+\nBXgf+AToT9IVuDaQ6RmntpA4PwXmAT0btfck+Y2gSV+NGYY6dmnQ1qFHfzr07J97gNXkqdc+oNtO\nFyxyfXPr6p193WOcfd1jeYZltsAnUxbdPVDo5DPO4uQzzipzNNXvf++4jbvvuK1B27RpUysUTdsW\nEdcVfHxD0nhghKTVI2JcscepeOKMiK8kvQTsBAwFUFKz7wRcuaj9OvX7HnVLrbR4gjQza6P23nd/\n9t634cxeI199hUHbb1mReOofR2nOl289zozRTzRomzcn8xidQhNIataeNKw6ewKvNLPf8+l+/YDq\nSZypy4Eb0wT6PMko267AjZUMyszMsimmj3Op9XZgqfV2aNA2Z+IYPr7lhJLOGRHjJE0gKbhGpnEs\nDWwBXNXMrhuR3OIdn+V8bSJxRsSQ9JnN80h+Q3gVGBQRkysbmZmZZSElSyn7Nb9e3Ugqw/ot15A0\nAPg8Ij4ErgB+I2kMyeMo5wMfAfek+68B/AQYBnwGDCAp2p5IR+EWrU0kToCIuBrI/CCqmZnVhE2B\nx0gqxCCZNAfgJuDwiLhEUlfgGmBZ4Elgt4JnOOcCO5M8ttIN+BC4HWh54EcjbSZxmplZO1DqK8Ja\n2CcinqCFuQci4hzgnEWs+wjYPntgC3PiNDOz3JTrVm1b4sRpZma5EaVNZlBFedOJ08zM8lMLFWfF\n56o1MzOrJq44zcwsN6oTdXUl3KotYZ9KceI0M7Pc1MKtWidOMzPLTTFT7i1qv2rhPk4zM7MMXHGa\nmVlufKvWzMwsg0q8yHpxc+I0M7P8lGnKvbbEidPMzHJTC7dqPTjIzMwsA1ecZmaWG89Va2ZmlkEt\n3Kp14jQzs9zUwqha93GamZll4IrTzMxy41u1ZmZmWfg5TjMzs+Ilo2pL269aOHGamVluPDjIzMzM\nGnDFaWZmufHgIDMzswxq4VatE6eZmeWnxIqzmkYHuY/TzMwsA1ecZmaWG0/ybmZmlkEtDA7yrVoz\nM8tN/eCgUpYijv0tSVdIek/STElPSdq00TbnSfokXf+wpH55X6MTp5mZ5aaciRO4HtgJOBBYH3gY\neERS7/TcpwHHAUcBmwMzgOGSOud5jU6cZmbW5knqAvwQOCUino6IsRFxLjAGOCbd7ETg/Ii4LyJe\nBw4GVgL2yjMWJ04zM8uPvunnzLIUMTqoI9ABmNOofRawtaTVgV7AiPoVETENeA4YmNflgROnmZnl\nSJR4q7aFzBkRXwL/Ac6S1FtSnaSfkiTF3iRJM4CJjXadmK7LjROnmZnlppRqM8NI3J+S1KYfA7NJ\n+jP/Bcwv2wU1wY+jmJnZYjXxpYeZ9PLDDdq+njWjxf0iYhywg6QlgaUjYqKkW4GxwASSpNqThlVn\nT+CVnEIHnDjNzCxHxYyQ7bXpLvTadJcGbdM/HM2Llx5W1DkiYhYwS1J3YBBwckSMkzSBZNTtyDSW\npYEtgKuyXkdznDjNzCw35ZwAQdIuJFXlaGAt4BLgTeDGdJMrgN9IGgO8B5wPfATckz2iRXPiNDOz\n3EiirnxvR1kG+B2wMvA5cAfwm4iYBxARl0jqClwDLAs8CewWEXMzB9QMJ04zM8tNOSvOiLgduL2F\nbc4BzskeQfE8qtbMzCwDV5xmZpYbvx3FzMwsAwnq2vnbUZw4zcwsNxkmbF9ov2rhPk4zM7MMiqo4\n02dnihIRD5UejpmZVbNaeJF1sbdqHyxyuyCZvd7MzGqQaHnC9kXtVy2KTZxLljUKMzNrFzw4KBUR\njd9/BoCkuohYrLPSm5mZVVLmwUHpO9BOkfQuMFvSGmn7YEkH5x6hmZlVjXK9j7MtKWVU7WnAscCF\nwNcF7W8DR+cRlJmZVacyv4+zTSglcR4GHBUR1wPzCtpfBdbNJSozM6tKdekk76Us1aKUCRD6kFSX\nTVmiFbGYmVm1K7V6rJ68WVLFORoY2ET73qQvDzUzM2uvSqk4fwtcI6kHSeL9nqR1gCNJkqeZmdWo\npL+ylCn3yhBMmWROnBFxh6QvgMEkg4OuIOnf3C8iHsg5PjMzqyLJ21FK269alDTJe0Q8AjwCIEkR\nEblGZWZmVanUgT7tfXAQAJLWB9ZLv34zIt7ILSozM7M2KnPilNQL+AewEzArbe4i6THgoIgYn2N8\nZmZWZaqndixNKaNqrwO6AxtFRLeI6AZsDCwDXJtncGZmVl1KmjWoxHd4Vkopt2p3AraOiNfqGyLi\nNUm/AJ7ILTIzM6s6dSVO8l7KPpVSSuL8ZBHtAUxoRSxmZlblSq0eq6niLOVW7enAn9LBQcCCgUJX\nkMxja2Zm1m4VVXFKGk9SUdbrDrwmqX5w0JLAXOCPwO25RmhmZlWliorHkhR7q/accgZhZmbtQy3c\nqi32RdbXlDsQMzOrfh4c1AJJdY2PERFzWxWRmZlZG5Z5cJCkJSVdKukDkn7NWY0WMzOrUfWTvGdf\nKh158UoZVfs7YE/gDJLEeWzaNhE4PL/QzMysGqmEpcVjSuMkzW9i+VO6/sYm1g0rw+WVdKt2b+Dw\niBgh6a/AIxExRtK7wD7ATblGaGZmVaOMk7xvCnQo+LwB8BAwJP0cwAPAoXyTi+dkDqQIpSTOFYB3\n0q+nkTyaAvA4cGUOMZmZWZUq12vFIuKzBttL3wfejYgnC5rnRMTk7GfPppRbteOAvunXo4Efpl8P\nIkmkZmZmZSOpE3AgcH2jVdtLmijpLUlXS1quHOcvpeL8B7AZ8BTwe+BuSccC3Uj6Pc3MrEYtpuc4\n9yZ5sUhh1+ADwJ0kxd2aJGNvhkkamPc7ozMnzoi4uODrB9Lp9jYDxkTE83kGZ2Zm1SUZVVvafhkc\nDjwQEQvmR4+IIQXr35D0X+BdYHvgsewRLVqrnuMEiIh3+KbP08zMapiKGBz0zlP3M+aphgNe586Y\nXuzx+wI7A3s1t11EjJP0KdCPSiROSUcVe8CI+Fvp4ZiZWXu31ta7s9bWuzdomzz2Te48db9idj+c\n5PHHZh81kbQKsDwwvsQwF6nYivPcIrcLwInTzKxGlfNWrZKO0EOBGyNifkF7N2AwSR/nBJIq82Lg\nbWB49miaV+xctb3zPnFrPX71EWy08caVDsMMgO6bHVfpEMwWmD+z7E9kLJIocXBQUdMgsDPQB7ih\nUfs8oD9wMLAsyXujhwNnR8RXmYNpQav7OM3MzOqJ0p5zLCZtRsTDNJwEob59NrBrCactiROnmZnl\npn6u2lL2qxal/GJgZmZWs1xxmplZbvw+TjMzswxUYuJs97dqJW0u6TpJj0laKW07QNKW+YZnZmbV\npLR3cZY2ErdSSnmR9Z7AE8ASwECgS7qqB/Cb/EIzMzNre0qpOAcDx0XEQUDh8zFPAZvkEpWZmVWl\nOr7p58y0VDrwDErp41wXGNFE+xd8825OMzOrQYtpkveKKiVxTgJWB95r1D6Q5HUuZmZWo4qZ5H1R\n+1WLUqrjG4ArJA0gmZt2eUn7AJfieWrNzGpaXSuWalFKxflboBPwH5KBQc8CXwNXRsQfcozNzMys\nzSnlRdbzgbMkXQSsA3wL+G9ETMk7ODMzqy7u42xGRMwAXs4xFjMzq3K10MeZOXFKavbloRHxvdLD\nMTOzaiZKrDhzj6R8Sqk432/0uROwIcmLQ//d6ojMzMzasFL6OI9pql3ShVTXLw1mZpazWpjkPc8R\nwDcAR+Z4PDMzqzL1fZxZl3bdx9mMjWk4BZ+ZmdUYj6ptgqR/NW4CegNbAZfkEZSZmVWnWrhVW0rF\n2fjy5gOvApdHxNDWh2RmZtZ2ZUqckjoAfwBGR8TU8oRkZmbVTO18nGimwUERMQ94Eli+POGYmVk1\n82vFmvYm0AcYm3MsZmZW5Wqhj7OUJH8qcKmknSV1l9S5cMk7QDMzs7aklIpzeKM/G+tQYixmZlbt\nSn0ms4qeRyklce6WexRmZtYu1MKt2qITp6SzgUsjYlGVppmZ1bhamAAhSx/nYJJ3b5qZmTVJlDjl\nXhU9wpIlcVbPVZmZWbsjaSVJ/5D0qaSZkl6TtHGjbc6T9Em6/mFJ/fKOI+uo2sg7ADMzaz9Keoaz\niH5RScsCTwNzgEHAesCvgSkF25wGHAccBWwOzACG5/3ER9bBQW9LajZ5RsRyrYjHzMyqWBn7OE8H\nPoiIIwraGr8f+kTg/Ii4LzmmDgYmAnsBQ7JH1bSsiXMw4Kn2zMysSXWIuhJ69orY5/vAg5KGANsB\nHwNXR8R1AJJWB3oBI+p3iIhpkp4DBlLBxHlrREzK6+RmZmZFWgM4BrgMuIDkVuyVkuZExD9IkmaQ\nVJiFJqZb6ye/AAAUmklEQVTrcpMlcbp/08zMmlfirdoiitQ64PmIOCv9/Jqk9YGjgX+UcMaSZUmc\nHlVrZmbNKmagzwsPDeWFRxq+hXLWl9NbOvR4YFSjtlHAD9OvJ5DkqZ40rDp7Aq+0dPAsik6cEVFN\nk9ebmVkF1D+X2ZwtBv2ALQb9oEHbB6Nf54JD92hut6eBdRq1rUM6QCgixkmaAOwEjASQtDSwBXBV\nhktoUSlT7pmZmS1SmWYB+gPwtKQzSAb6bAEcARxZsM0VwG8kjQHeA84HPgLuyTMQJ04zM2vzIuJF\nSXsDFwFnAeOAEyPi1oJtLpHUFbgGWJbk/dG7RcTcPGNx4jQzs9wkfZwlPI5SxC4RMQwY1sI25wDn\nZA4gAydOMzPLTS1M8u7EaWZmuRHZ53Kt369aeKSsmZlZBq44zcwsN5JQCfddS9mnUpw4zcwsN6K0\n267VkzadOM3MLEfFTICwqP2qhfs4zczMMnDFaWZmuaqe2rE0TpxmZpYbUeJznLlHUj5OnGZmlhuP\nqjUzM8ugjtIGz1TTgJtqitXMzKziXHGamVl+SrxVW02T1TpxmplZbjwBgpmZWQbJ21FKGRxUhmDK\nxH2cZmZmGbjiNDOz3NTCqFonTjMzy48HB5mZmRXPg4PMzMwyqIUp96rptrKZmVnFueI0M7Pc1CHq\nSqgfS9mnUpw4zcwsPypxnE/15E0nTjMzy4/S/0rZr1q4j9PMzCwDV5xmZpYblXirtooe43TiNDOz\n/HhwkJmZWRY1MDjIfZxmZpab+lu1pSzFn0OnS5ov6fKCthvStsJlWDmu0RWnmZlVDUmbAUcBrzWx\n+gHgUL6pX+eUIwZXnGZmlptkrtpS/ivi2NK3gH8CRwBfNLHJnIiYHBGT0mVqntdWz4nTzMxyUwfU\nqYSluMNfBdwbEY8uYv32kiZKekvS1ZKWy+myGnDibOeeeupJ9t17T9ZYdWW6dq7jvnuHLrTNeeec\nzep9V2K5pbuy+67f5d0xYyoQqbVXW220Jrdf8XPeHf5bZrx0Jbtvt0GD9WcetRuv3PkbJj99GR8/\nfjH3/eU4Nv32qgvW9+3dnRkvXcmXL17JjJcaLnvttOHivhxrUan1ZvM1p6QDgA2BMxaxyQPAwcCO\nwKnAdsAwlfSOs+Y5cbZzM2fMoP+ADfnjn65u8h15l/7+Yv569Z+56i9/48lnnqdrt258f/dBzJ07\ntwLRWnvUdcnOjBz9ESf+7jYiFl7/zvuT+OVFQ9hk3wvY8bDLef+Tz7j3L8ey3DLdAPhg/BRW2/lM\nVv/umay2c7Kc/9dhTJ8xh+FPv7GYr8YqQdIqwBXAgRHxVVPbRMSQiLgvIt6IiKHAHsDmwPZ5x+PB\nQe3cLoN2ZZdBuwIQTfzUuupPf+T0M8/ie7vvAcD1N9zMqiv3ZOg9d7Pvfj9arLFa+/TwM6N4+JlR\nQNMjJ28f/lKDz6dddheH7jWQ9ddaif978R0AJk/5ssE2e+4wgDseeplZs5v8GWoVVMwI2RH33cmj\n99/VoG3G9GnN7bIJsCLwckEF2QHYVtJxwBLR6AdcRIyT9CnQD3gsyzW0xImzhr03bhwTJ0xghx13\nWtC29NJLs9nmW/Dcs/9x4rTFrmPHOo7YZ2u+mD6L/779cZPbbLReHwasszInXnjrYo7OilHMUJ+d\n99iXnffYt0Hb22+8xs/32XFRuzwCbNCo7UZgFHBR46QJC6rU5YHxRQWeQZtInJK2AU4h+a2iN7BX\nWmpbGU2YMAFJ9OjZs0F7j549mThxQoWislq069bf5uaLDqNrl86MnzyVPY75M1OmzWxy20P2Gsio\nsRN44fX3F3OUVoz6wT6l7LcoETEDeLOwTdIM4LOIGCWpGzAYuBOYQFJlXgy8DQzPHk0LseZ9wBJ1\nA14FfgE00QtiZu3Z4y+8zeb7/47tD7mMh555k1su+RnLL9ttoe2W6NyRH+26KTf+7zMViNKKU57B\nQU0ozBXzgP7APcBo4FrgBWDbRfWJtkabqDgj4kHgQYByjICypvXq1YuIYNLEifQsqDonTZzIgA03\nqmBkVmtmz/mK9z7+jPc+/owX33ifkXefzaF7DeSyGx9psN0Pv7sRSy7RiX/d/0KFIrW2IiJ2LPh6\nNrDr4jp3W6k4rQJWW311evbqxWOPjljQNm3aNF54/jm2HPidCkZmta5OonPnhX+vP+QHA7n/if/y\n+dQZFYjKirE4ptyrtDZRcVr5zJgxg3fHjFkwonbc2LGMfO01ui+3HH369OG4E37Jxb/7LWv268eq\nq67GueecxcqrrML39/xBhSO39qJrl86s2XfFBTfiVl95eTZYe2WmTJ3BZ1NncNrPduX+J0Yy4dNp\nLL9sN47efzt6r7gMdz38SoPjrNFnBbbeuB97Hnv14r8IK1pJN11L3KdSnDjbuZdfepFBO++AJCRx\n+qm/BuCnBx3CNdf9nV+ffCqzZs7kuF/8nKlffMFWW2/DPfc+QOfOnSscubUXG3+7L8P/dgIREAEX\n/eqHAPzz3uc44cLbWGf1nhy4xxEsv2w3Pp86g5fe+ICdDr+c0eMmNjjOwXsO5MMJn/Poc29V4jKs\nSHUSdSWUj6XsUylq6tm+SpI0n2ZG1UraGHhp6222ZZlllmmwbr/9f8z+B/x4MURp1lD3zY6rdAhW\no+ZNeZt5U95p0Bbz5hIzPgHYJCJeXhxx1P9svuHux1nn2wMy7z/6jdc4bK/tYTHGXKqqrTgvufQP\nbLTxxpUOw8ysojp0X5sO3ddu0DZ/5mTmvj2kIvH4Vu1ikj6D049vvndrSBoAfB4RH1YuMjMzy6ya\nsmAJ2kTiBDYlmRIp0uWytP0m4PBKBWVmZtkV95Kw6tUmEmdEPIEfjTEzq3qlPlpSRWODnKzMzMyy\naBMVp5mZtQ8eHGRmZpZFDWROJ04zM8tNMa8VW9R+1cJ9nGZmZhm44jQzs9zUwqhaJ04zM8tVFeXA\nkjhxmplZfjw4yMzMrHgeHGRmZmYNuOI0M7PciBIHB+UeSfk4cZqZWW5qoIvTidPMzHJUA5nTfZxm\nZmYZuOI0M7Pc1MKoWidOMzPLT4kzB1VR3nTiNDOz/NRAF6cTp5mZ5agGMqcHB5mZmWXgitPMzHJT\nC4ODXHGamVlu6l8rVsrS/HF1tKTXJE1Nl2ck7dpom/MkfSJppqSHJfUrxzU6cZqZWW7UiqUFHwKn\nARsDmwCPAvdIWg9A0mnAccBRwObADGC4pM55XVs9J04zM2vzIuL+iHgwIt6NiDER8RvgS2DLdJMT\ngfMj4r6IeB04GFgJ2CvvWJw4zcwsX2UoNxscXqqTdADQFXhG0upAL2BE/TYRMQ14DhjYuotZmAcH\nmZlZrso10EfS+sB/gC7AdGDviBgtaSAQwMRGu0wkSai5cuI0M7PcFDPQZ1H7FeEtYACwDLAvcLOk\nbbOfrXWcOM3MLDfF3Hm973+HcN/dtzdomz5taovHjoivgbHpx1ckbU7St3lJetqeNKw6ewKvFBV4\nBk6cZma2WO2x94/YY+8fNWh7Y+Qr/HDQ1lkPVQcsERHjJE0AdgJGAkhaGtgCuKr1ETfkxGlmZvkp\n05R7ki4EHgA+AJYCDgS2A3ZJN7kC+I2kMcB7wPnAR8A9JUTTLCdOMzPLTRlnDuoB3AT0BqaSVJa7\nRMSjABFxiaSuwDXAssCTwG4RMTdzMC1w4jQzs9yUa3BQRBzR0jEi4hzgnOxnz8bPcZqZmWXgitPM\nzHJVPdO1l8aJ08zM8lMD7+N04jQzs9zUwmvFnDjNzCw3osTBQblHUj4eHGRmZpaBK04zM8tNDXRx\nOnGamVmOaiBzOnGamVluamFwkPs4zczMMnDFaWZm+Slxyr0qKjidOM3MLD810MXpxGlmZvkp1yTv\nbYn7OM3MzDJwxWlmZjlq/zdrnTjNzCw3tXCr1onTzMxy0/7rTSdOMzPLWTVVj6Xw4CAzM7MMXHGa\nmVluamHKPSdOMzPLTw10cjpxmplZbmogb7qP08zMLAtXnGZmlhs/x2lmZpZBcqu2lMFB1cOJ08zM\n8lMDnZxOnGZmlqsqyoEl8eAgMzOzDJw4zcwsN/WDg0pZmj+utpE0VNLHkuZL2rPR+hvS9sJlWDmu\n0bdqzcwsN2WcOagb8CpwPXDXIrZ5ADiUb+4Wz8kcSBGcOM3MLDeixMdRWlgfEQ8CDwJIizzDnIiY\nnP3s2fhWrZmZtRfbS5oo6S1JV0tarhwnccVpZmbtwQPAncA4YE3gd8AwSQMjIvI8kROnmZnlplIz\nB0XEkIKPb0j6L/AusD3wWOuO3pATp5mZ5ajlwUF3DPk3dwy5tUHbtKlTc40iIsZJ+hTohxOnmZm1\nVcVUnPvt/2P22//HDdpefeVltvvOZjnGoVWA5YHxuR005cRpZmZtnqRuJNVjfVpeQ9IA4PN0GUzS\nxzkh3e5i4G1geN6xOHGamVluyjhV7aYkt1wjXS5L228CfgH0Bw4GlgU+IUmYZ0fEVyWE0ywnTjMz\ny0+ZMmdEPEHzj1DuWsJZS+LEaWZmuSnjzEFthidAMDMzy8CJs8bdduu/Kx2CWQPzprxd6RCsFco1\nyXtb4sRZ426/zYnT2pZ5U96pdAjWSiphqSbu4zQzs/yUcVhtW+HEaWZmufHgIDMzM2ugGivOLgBj\n3xnFEtUYfRszfdpU3hz5cqXDqHpPX394pUNoN0466b/84Q/+frbGqFGj+OlPh0D683JxGv3WqJJq\nx9Fvjco9lnJRzm9bKTtJPwFuqXQcZmZV4MCI+NfiOJGkvsAooGsrDjMTWC8iPsgnqvKoxsS5PDAI\neA+YXdlozMzapC7AasDwiPhscZ00TZ4rtOIQn7b1pAlVmDjNzMwqyYODzMzMMnDiNDMzy8CJ08zM\nLAMnzhol6VhJ4yTNkvSspPxevW6WkaRtJA2V9LGk+ZL2rHRMZovixFmDJO1P8hLYwcBGwGvAcEmt\nGQ1n1hrdgFdJXkjsEYvWpnlUbQ2S9CzwXEScmH4W8CFwZURcUtHgrOZJmg/sFRFDKx2LWVNccdYY\nSZ2ATYAR9W2R/Pb0CDCwUnGZmVULJ87aswLQAZjYqH0i0Gvxh2NmVl2cOM3MzDJw4qw9nwLzgJ6N\n2nsCExZ/OGZm1cWJs8ZExFfAS8BO9W3p4KCdgGcqFZeZWbXwi7lq0+XAjZJeAp4HTiJ5o8GNlQzK\napekbkA/WPBGqjUkDQA+j4gPKxeZ2cL8OEqNkvQL4FSSW7SvAsdHxIuVjcpqlaTtgMdY+BnOmyLC\nL+e0NsWJ08zMLAP3cZqZmWXgxGlmZpaBE6eZmVkGTpxmZmYZOHGamZll4MRpZmaWgROnmZlZBk6c\nZmZmGThxmpmZZeDEaTVB0qqS5kvqn37eTtI8SUtXIJbHJF3ezPrBkl7JeMz5kvZsZVw3SLqrNccw\nqwVOnFYx6Q/q+WkCmyPpHUlnSSrXv8vC+SWfBnpHxLRidmwp2ZWB58I0a6P8dhSrtAeAQ4EuwG7A\n1cAc4JLGG6YJNaL0CZbr37xBRHwNTCrxOGZWw1xxWqXNiYjJEfFhRPwNeAT4AYCkQyVNkfR9SW8A\ns4E+6bojJL0paVb65zGFB5W0uaSX0/XPAxtRUMWlt2rnF96qlbRVWlnOkPS5pAckLSPpBmA74MSC\nCrlvus/6koZJmi5pgqSbJS1fcMyuadt0SR9L+lXWb5CkTSU9JGmypC8kPS5poyY2XSmNZaakdyXt\n0+g4q0i6Lf2efibpbkmrZo3HrNY5cVpbMxvonH4dJO8JPRX4GfBtYJKkA4FzgDOAdYEzgfMkHQQL\n3u14L/A6sHG67aVNnKswkW5IkrRfB7YEBgL3AB2AE4H/ANeSvIatN/ChpGWAESQvBt8YGAT0AIYU\nnONSYBvg+8AuwPbptlksRfKu1O8AWwBvA8PS6yx0HnA70B+4BbhV0jrp9XUEhgNTga3SY00HHkzX\nmVmR/D+MtRmSdiZJPn8saO4IHBMRrxdsdw7w64i4J216X9K3gZ8D/wAOJLkte0REzAVGSepDcht4\nUU4BXoiI4wvaRheccy4wMyImF7QdB7wcEWcVtB0BfCCpHzAeOBz4SUQ8nq4/BPioiG/HAhHxWOFn\nSUcD+5NUwcMKVg2JiBvSr8+W9F3geOA44ACS1wgeVXCcnwFTSJL5I1liMqtlTpxWad+XNB3oRJLs\nbgHOLVg/t1HS7AqsCVwv6bqC7TqSJAFIqtCRadKs958W4tiQhpViMQYAO6bxF4o0xq4k1/X8ghUR\nUySNJgNJPYALSBJlD5IqeEmgb6NNn230+T9pjJBUoWs1EesSaaxOnGZFcuK0SnsUOBr4CvgkIuY3\nWj+r0edvpX8eQUFCSs1rRRyNz1OMbwFDSW4lq9G68cBarYin0M1Ad5Lq8QOSwVPP8s0t7WJ8C3gR\n+AkLxzp54c3NbFHcx2mVNiMixkXER00kzYVExCTgE2DNiBjbaHk/3WwU0F9SYWIZ2MKhRwI7NbN+\nLkmlV+hlkn7X95uIZRbwLvA1Sb8kAJK6A2u3dJ2NfAe4MiKGR8Qokl8yVmhiuy2b+DyqINa1gMlN\nxNq4CjWzZjhxWjUaDJwh6XhJa6UjWw+VdFK6/l8kt0uvk7SepO8Bv27iOIWV1++AzSRdJWkDSetK\nOlrScun694At0okU6kfNXgUsRzIIZ1NJa0gaJOnvkhQRM4Drgd9L2kHS+sANZK+M3wEOSmPaAvgn\nMLOJ7faTdFj6PTkX2Az4c7ruFuBT4B5JW0taTdL2kv4oaaWM8ZjVNCdOqzoRcT3JrdrDSCrFx4FD\ngLHp+hkko1jXJ6m0zie5nbrQoQqO+Q7JqNf+wHMkEyTsSVIxQjI6dh7wJsnI3r4RMZ5khGodyYjV\nkcDlwJSCZ01PAZ4kuaX7UPr1Sxkv+XCSW7UvATeRDJ5q/AxqkPxCcQDwGvBT4ICIeCu9vlnAtiS3\neu9Mr+Nakj7OoiaBMLOESn+W3MzMrPa44jQzM8vAidPMzCwDJ04zM7MMnDjNzMwycOI0MzPLwInT\nzMwsAydOMzOzDJw4zczMMnDiNDMzy8CJ08zMLAMnTjMzswycOM3MzDL4/7N/xt251f2ZAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21638ec95c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import itertools\n",
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "lr.fit(X_train_undersample,y_train_undersample.values.ravel())\n",
    "y_pred_undersample = lr.predict(X_test_undersample.values)\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test_undersample,y_pred_undersample)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print(\"Recall metric in the testing dataset: \", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the testing dataset:  0.918367346939\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAGbCAYAAAAcMUOXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21638ebeac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "lr.fit(X_train_undersample,y_train_undersample.values.ravel())\n",
    "y_pred = lr.predict(X_test.values)\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print(\"Recall metric in the testing dataset: \", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "C parameter:  0.01\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.492537313433\n",
      "Iteration  2 : recall score =  0.602739726027\n",
      "Iteration  3 : recall score =  0.683333333333\n",
      "Iteration  4 : recall score =  0.569230769231\n",
      "Iteration  5 : recall score =  0.45\n",
      "\n",
      "Mean recall score  0.559568228405\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  0.1\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.567164179104\n",
      "Iteration  2 : recall score =  0.616438356164\n",
      "Iteration  3 : recall score =  0.683333333333\n",
      "Iteration  4 : recall score =  0.584615384615\n",
      "Iteration  5 : recall score =  0.525\n",
      "\n",
      "Mean recall score  0.595310250644\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  1\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.55223880597\n",
      "Iteration  2 : recall score =  0.616438356164\n",
      "Iteration  3 : recall score =  0.716666666667\n",
      "Iteration  4 : recall score =  0.615384615385\n",
      "Iteration  5 : recall score =  0.5625\n",
      "\n",
      "Mean recall score  0.612645688837\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  10\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.55223880597\n",
      "Iteration  2 : recall score =  0.616438356164\n",
      "Iteration  3 : recall score =  0.733333333333\n",
      "Iteration  4 : recall score =  0.615384615385\n",
      "Iteration  5 : recall score =  0.575\n",
      "\n",
      "Mean recall score  0.61847902217\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  100\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.55223880597\n",
      "Iteration  2 : recall score =  0.616438356164\n",
      "Iteration  3 : recall score =  0.733333333333\n",
      "Iteration  4 : recall score =  0.615384615385\n",
      "Iteration  5 : recall score =  0.575\n",
      "\n",
      "Mean recall score  0.61847902217\n",
      "\n",
      "*********************************************************************************\n",
      "Best model to choose from cross validation is with C parameter =  10.0\n",
      "*********************************************************************************\n"
     ]
    }
   ],
   "source": [
    "best_c = printing_Kfold_scores(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the testing dataset:  0.619047619048\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAGbCAYAAAAcMUOXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x216369961d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "lr.fit(X_train,y_train.values.ravel())\n",
    "y_pred_undersample = lr.predict(X_test.values)\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred_undersample)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print(\"Recall metric in the testing dataset: \", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the testing dataset:  1.0\n",
      "Recall metric in the testing dataset:  1.0\n",
      "Recall metric in the testing dataset:  1.0\n",
      "Recall metric in the testing dataset:  0.986394557823\n",
      "Recall metric in the testing dataset:  0.931972789116\n",
      "Recall metric in the testing dataset:  0.884353741497\n",
      "Recall metric in the testing dataset:  0.836734693878\n",
      "Recall metric in the testing dataset:  0.748299319728\n",
      "Recall metric in the testing dataset:  0.571428571429\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAPeCAYAAAD6bcIrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZb+KyndGwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x216397b1dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LogisticRegression(C = 0.01, penalty = 'l1')\n",
    "lr.fit(X_train_undersample,y_train_undersample.values.ravel())\n",
    "y_pred_undersample_proba = lr.predict_proba(X_test_undersample.values)\n",
    "\n",
    "thresholds = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "j = 1\n",
    "for i in thresholds:\n",
    "    y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > i\n",
    "    \n",
    "    plt.subplot(3,3,j)\n",
    "    j += 1\n",
    "    \n",
    "    # Compute confusion matrix\n",
    "    cnf_matrix = confusion_matrix(y_test_undersample,y_test_predictions_high_recall)\n",
    "    np.set_printoptions(precision=2)\n",
    "\n",
    "    print(\"Recall metric in the testing dataset: \", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))\n",
    "\n",
    "    # Plot non-normalized confusion matrix\n",
    "    class_names = [0,1]\n",
    "    plot_confusion_matrix(cnf_matrix\n",
    "                          , classes=class_names\n",
    "                          , title='Threshold >= %s'%i) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "credit_cards=pd.read_csv('creditcard.csv')\n",
    "\n",
    "columns=credit_cards.columns\n",
    "# The labels are in the last column ('Class'). Simply remove it to obtain features columns\n",
    "features_columns=columns.delete(len(columns)-1)\n",
    "\n",
    "features=credit_cards[features_columns]\n",
    "labels=credit_cards['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_train, features_test, labels_train, labels_test = train_test_split(features, \n",
    "                                                                            labels, \n",
    "                                                                            test_size=0.2, \n",
    "                                                                            random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "oversampler=SMOTE(random_state=0)\n",
    "os_features,os_labels=oversampler.fit_sample(features_train,labels_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227454"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os_labels[os_labels==1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "C parameter:  0.01\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.890322580645\n",
      "Iteration  2 : recall score =  0.894736842105\n",
      "Iteration  3 : recall score =  0.968861347792\n",
      "Iteration  4 : recall score =  0.957595541926\n",
      "Iteration  5 : recall score =  0.958430881173\n",
      "\n",
      "Mean recall score  0.933989438728\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  0.1\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.890322580645\n",
      "Iteration  2 : recall score =  0.894736842105\n",
      "Iteration  3 : recall score =  0.970410534469\n",
      "Iteration  4 : recall score =  0.959980655302\n",
      "Iteration  5 : recall score =  0.960178498807\n",
      "\n",
      "Mean recall score  0.935125822266\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  1\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.890322580645\n",
      "Iteration  2 : recall score =  0.894736842105\n",
      "Iteration  3 : recall score =  0.970454796946\n",
      "Iteration  4 : recall score =  0.96014552489\n",
      "Iteration  5 : recall score =  0.960596168431\n",
      "\n",
      "Mean recall score  0.935251182603\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  10\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.890322580645\n",
      "Iteration  2 : recall score =  0.894736842105\n",
      "Iteration  3 : recall score =  0.97065397809\n",
      "Iteration  4 : recall score =  0.960343368396\n",
      "Iteration  5 : recall score =  0.960530220596\n",
      "\n",
      "Mean recall score  0.935317397966\n",
      "\n",
      "-------------------------------------------\n",
      "C parameter:  100\n",
      "-------------------------------------------\n",
      "\n",
      "Iteration  1 : recall score =  0.890322580645\n",
      "Iteration  2 : recall score =  0.894736842105\n",
      "Iteration  3 : recall score =  0.970543321899\n",
      "Iteration  4 : recall score =  0.960211472725\n",
      "Iteration  5 : recall score =  0.960903924995\n",
      "\n",
      "Mean recall score  0.935343628474\n",
      "\n",
      "*********************************************************************************\n",
      "Best model to choose from cross validation is with C parameter =  100.0\n",
      "*********************************************************************************\n"
     ]
    }
   ],
   "source": [
    "os_features = pd.DataFrame(os_features)\n",
    "os_labels = pd.DataFrame(os_labels)\n",
    "best_c = printing_Kfold_scores(os_features,os_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the testing dataset:  0.90099009901\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAGbCAYAAAAcMUOXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2164f604be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "lr.fit(os_features,os_labels.values.ravel())\n",
    "y_pred = lr.predict(features_test.values)\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(labels_test,y_pred)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print(\"Recall metric in the testing dataset: \", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
